Theory, Abstraction and Design in Medical Informatics

Dental Informatics: An Emerging Biomedical Informatics Discipline

Biomedical informatics is a maturing discipline. During the last forty years, it has developed into a research discipline of significant scale and scope. One of its subdisciplines, dental informatics, is beginning to emerge as its own entity. While there is a growing cadre of trained dental informaticians, dental faculty and administrators in general are not very familiar with dental informatics as an area of scientific inquiry. Many confuse informatics with information technology (IT), are unaware of its scientific methods and principles, and cannot relate dental informatics to biomedical informatics as a whole. This article delineates informatics from information technology and explains the types of scientific questions that dental and other informaticians typically explore. Scientific investigation in informatics centers primarily on model formulation, system development, system implementation, and the study of effects. Informatics draws its scientific methods mainly from information science, computer science, cognitive science, and telecommunications. Dental informatics shares many types of research questions and methods with its parent discipline, biomedical informatics. However, there are indications that certain research questions in dental informatics require novel solutions that have not yet been developed in other informatics fields.

Defining Biomedical Informatics Competency: The Foundations of a Profession

Is biomedical informatics a science or a profession? This question has been asked of many members in the biomedical informatics community, yet we still lack a response that galvanizes our community. We debate the issues over lunch. We create long, multi-threaded e-mail discussions, we write papers on the topic, and still we aren’t able to convince ourselves—let alone the rest of the scientific community. In this paper, I will describe a curriculum model for biomedical informatics and research that is developing at Columbia University, Department of Biomedical Informatics (DBMI). We believe that a strong educational foundation creates competent professionals who, in turn, comprise a bioinformatics culture. The outcome of DBMI’s curriculum design and competency project will be a set of biomedical informatics competencies which we believe will define the core knowledge and skills of the field.

Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education

Introduction. The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze. The medical curriculum is the main tool used by teachers and directors to plan, design, and deliver teaching and assessment activities and student evaluations in medical education in a continuous effort to improve it. Big data remains largely unexploited for medical education improvement purposes. The emerging research field of visual analytics has the advantage of combining data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is a lack of research on the use and benefits of visual analytics in medical education.

Methods. The present study is based on analyzing the data in the medical curriculum of an undergraduate medical program as it concerns teaching activities, assessment methods and learning outcomes in order to explore visual analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes. Cytoscape software was employed to build networks of the identified aspects and visualize them.

Results. After the analysis of the curriculum data, eleven aspects were identified. Further analysis and visualization of the identified aspects with Cytoscape resulted in building an abstract model of the examined data that presented three different approaches; (i) learning outcomes and teaching methods, (ii) examination and learning outcomes, and (iii) teaching methods, learning outcomes, examination results, and gap analysis.

Discussion. This study identified aspects of medical curriculum that play an important role in how medical education is conducted. The implementation of visual analytics revealed three novel ways of representing big data in the undergraduate medical education context. It appears to be a useful tool to explore such data with possible future implications on healthcare education. It also opens a new direction in medical education informatics research.

Medical informatics and bioinformatics: European efforts to facilitate synergy

Over the past decade there have been several attempts to rethink the basic strategies and scope of medical informatics. Meanwhile, bioinformatics has only recently experienced a similar debate about its scientific character. Both disciplines envision the development of novel diagnostic, therapeutic, and management tools, and products for patient care. A combination of the expertise of medical informatics in developing clinical applications and the focused principles that have guided bioinformatics could create a synergy between the two areas of application. Such interaction could have a great influence on future health research and the ultimate goal, namely continuity and individualization of health care. This article summarizes current activities related to facilitating synergy between medical informatics and bioinformatics, emphasizing activities in Europe while relating them to efforts in other parts of the world. The report provides examples of the analysis that European investigators are carrying out, aiming to propose new ideas for collaborations between medical informatics and bioinformatics researchers in a variety of areas.